Dataset construction for shared adjacency matrix and varying node features

❓ Questions & Help

First of all, thank you for the package - it is very well designed! I am trying to use it for my problem and aim to use PyTorch Geometric to implement new architectures.

The examples in the documentation all talk about creating Data with edge_index, x, etc. In my case, the underlying adjacency matrix is the same across the dataset and I only have varying node features (and labels).

I want to use a PyG model and setup dataset correctly such that I vary the node features and share the underlying graph topology (crucially without copying the graph len(dataset) times).

I was thinking of doing this by passing the adjacency matrix in the constructor of a Dataset class and using that in the getitem method - I wanted to know if there are any caveats to this approach or if it violates any best practices?

[rusty1s]

So, what I mean is:

conv = GCNConv(in_channels, out_channels, node_dim=1)
conv(x, edge_index)

where x is a [batch_size, num_nodes, num_features] tensor and edge_index holds indices < num_nodes. For datasets, you can then use something like this

class MyDataset(torch.utils.Dataset)
    def __init__(...):
        self.edge_index = ...
        self.x_all = ...
    def __getitem__(self, idx):
        return x_all[idx]

and use the regular PyTorch DataLoader to create batches for x.

[chnsh]

If you have a graph like so: G = nx.fast_gnp_random_graph(100, 0.1) and if you use torch_geometric.from_networkx(G), because my graph is already integer ordered, and because networkx implements nodes as a dictionary, it is not ordered and hence the graph gets distorted